SPSS Statistics is loaded with powerful analytic techniques and time-saving capabilities to help you quickly and easily find new insights in your data.

Here's a look at the newest features and enhancements designed to help you:

Gain deeper predictive insights from large and complex datasets.

Reveal relationships and trends hidden in geospatial data.

Speed deployment and return on investment.

Discover causal relationships in time series data

Uncover hidden causal relationships among large numbers of time series using the Temporal Causal Modeling (TCM) technique. SPSS Statistics enables you to feed many time series into TCM to find out which series are causally related, and can automatically determine the best predictors for each target series.

Integrate, explore and model location and time data

SPSS Statistics includes geospatial analytics capabilities to help you explore the relationship between data elements that are tied to a geographic location.

Discover trends over time and space - Use the Spatio-Temporal Prediction (STP) technique to fit linear models for measurements taken over time at locations in 2D and 3D space, so you can predict how those areas may change over time.

Create association rules that incorporate geospatial attributes - Find associations between spatial and non-spatial attributes using the Generalized Spatial Association Rule (GSAR). It uses historical data such as location, type of event and the time an event happened to describe the occurrences of events, such as crimes or disease outbreaks.

Choose from a wider range of R programming options

Develop and test R programs using a full-featured, integrated R development environment within SPSS Statistics. You can also write R functions that use SPSS Statistics functionality with command syntax from within R, and return results to R.

Supports enterprise users who need to access the software with their employee identification badges and badge readers.

What's new in version 23?

Geospatial Association Rules

Using geospatial association rules, you can find patterns in data based on both the spatial and non-spatial properties. For example, you might identify patterns in crime data by location and demographic attributes. From these patterns, you can build rules that predict where certain types of crimes are likely to occur.

This procedure is available in the Base Statistics option.

Spatial Temporal Prediction

Spatial temporal prediction uses data that contains location data, input fields for prediction (predictors), a time field, and a target field. Each location has numerous rows in the data that represents the values of each predictor at each time interval at each location.

This procedure is available in the Base Statistics option.

Temporal Causal Models

Temporal causal modeling attempts to discover key causal relationships in time series data. In temporal causal modeling, you specify a set of target series and a set of candidate inputs to those targets. The procedure then builds an autoregressive time series model for each target and includes only those inputs that have a causal relationship with the target. This approach differs from traditional time series modeling where you must explicitly specify the predictors for a target series. Since temporal causal modeling typically involves building models for multiple related time series, the result is referred to as a model system.

Temporal causal modeling procedures are available in the Forecasting option.

Bulk Loading to a database

When you export data to a database, bulk loading submits data to the database in batches instead of one record at a time. This action can make the operation much faster, particularly for large data files.

Programmability enhancements

You can now run R programs that use functions in the R Integration Package for IBM® SPSS® Statistics from any external R process, such as an R IDE or the R interpreter. You can also now run SPSS Statistics command syntax from R.

Extension commands that are implemented in Python or R now support the use of the TO and ALL keywords in variable lists.

IBM SPSS Statistics - Essentials for R and IBM SPSS Statistics - Essentials for Python now include many more extension commands, with associated custom dialogs. Also, help for all extension commands that are installed with Essentials for R and Essentials for Python is now available by pressing the F1 key in the syntax editor.

IBM® SPSS® Statistics Faculty Pack 23

Windows:

Supported Operating Systems

Windows 10 Enterprise (32/64-Bit)

Windows 10 Education (32/64-Bit)

Windows 10 Pro (32/64-Bit)

Windows 10 Home (32/64-Bit)

Windows 8.1 Enterprise (32/64-Bit)

Windows 8.1 Professional (32/64-Bit)

Windows 8 Enterprise (32/64-Bit)

Windows 8 Professional (32/64-Bit)

Windows 7 Enterprise (32/64-Bit)

Windows 7 Professional (32/64-Bit)

Windows Vista Business (32/64-Bit)

Windows Vista Enterprise (32/64-Bit)

Windows XP Professional (32/64-Bit)

Hardware Requirements

Hardware

Components

Requirement

Disk Space

Desktop

IBM SPSS Statistics Client

2 gigabytes (GB) of available hard-disk space. If you install more than one help language, each additional language requires 60-70MB disk space.

Memory

Desktop

IBM SPSS Statistics Client

4 gigabyte(GB) of RAM or more is required, 8 gigabyte(GB) of RAM or more is recommended for 64-bit Client platforms.

Because the installer extracts files before installing, the same amount of temporary disk space is also needed for the installer. If you do not have enough space in /tmp or the installing users home directory, use the IATEMPDIR environment variable to specify a different temporary location for the extracted installer files. You can remove this folder after installation.

Additional free disk space is required to run the program (for temporary files). The amount of space needed for temporary files depends on the number of users, the expected size of the .sav file, and the procedure. You can use the following formula to estimate the space needed: * * , where can range from 1 to 2.5. For example, for procedures like K-Means Cluster Analysis (QUICK CLUSTER), Classification Tree (TREE), and Two-Step Cluster Analysis (TWOSTEP CLUSTER), the is closer to 1 than 2.5. If sorting is involved, it is 2.5. So, if you have four users, the expected .sav file size is 100 MB, and sorting is involved, you should allow 1 GB (4 Ã— 100 MB Ã— 2.5) of storage for temporary files.

Mac:

Supported Operating Systems

Mac OS X Mountain Lion 10.8
Mac OS X Mavericks 10.9

Mac OS X Yosemite 10.10

Mac OSX El Capitan 10.11

Note: Java JRE (6 or 7) must already be installed before you can run the SPSS Statistics 23 Mac silent installer.

Hardware Requirements

Hardware

Components

Requirement

Disk Space

Desktop

IBM SPSS Statistics Client

2 gigabytes (GB) of available hard-disk space. If you install more than one help language, each additional language requires 60-70MB disk space.

Memory

Desktop

IBM SPSS Statistics Client

4 gigabyte(GB) of RAM or more is required, 8 gigabyte(GB) of RAM or more is recommended for 64-bit Client platforms.

Because the installer extracts files before installing, the same amount of temporary disk space is also needed for the installer. If you do not have enough space in /tmp or the installing users home directory, use the IATEMPDIR environment variable to specify a different temporary location for the extracted installer files. You can remove this folder after installation.

Additional free disk space is required to run the program (for temporary files). The amount of space needed for temporary files depends on the number of users, the expected size of the .sav file, and the procedure. You can use the following formula to estimate the space needed: * * , where can range from 1 to 2.5. For example, for procedures like K-Means Cluster Analysis (QUICK CLUSTER), Classification Tree (TREE), and Two-Step Cluster Analysis (TWOSTEP CLUSTER), the is closer to 1 than 2.5. If sorting is involved, it is 2.5. So, if you have four users, the expected .sav file size is 100 MB, and sorting is involved, you should allow 1 GB (4 Ã— 100 MB Ã— 2.5) of storage for temporary files.